Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language ...

Google's TurboQuant algorithm compresses LLM key-value caches to 3 bits with no accuracy loss. Memory stocks fell within ...

A small error-correction signal keeps compressed vectors accurate, enabling broader, more precise AI retrieval.

At its core, the TurboQuant algorithm minimizes the space required to store memory while also preserving model accuracy. To ...

Memory prices are plunging and stocks in memory companies are collapsing following news from Google Research of a ...

That much was clear in 2025, when we first saw China's DeepSeek — a slimmer, lighter LLM that required way less data center ...

With TurboQuant, Google promises 'massive compression for large language models.' ...

Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 ...

The compression algorithm works by shrinking the data stored by large language models, with Google’s research finding that it can reduce memory usage by at least six times “with zero accuracy loss.” [ ...

Google unveils TurboQuant, PolarQuant and more to cut LLM/vector search memory use, pressuring MU, WDC, STX & SNDK.

Google LLC has unveiled a technology called TurboQuant that can speed up artificial intelligence models and lower their ...

The Google Research team developed TurboQuant to tackle bottlenecks in AI systems by using "extreme compression".